Systems Integration & Data Infrastructure
Connect ERP, CRM, e-commerce and data sources through APIs and pipelines so data moves without hands.
Every business runs on systems that were bought separately. Integration is how they become one operation: orders flow to the ERP, customers to the CRM, stock to the webshop and all of it to a warehouse where reporting and AI can use it.
We design an integration map first, then build with middleware, APIs and pipelines that are documented, monitored and easy to change. The aim is fewer point-to-point links and one clear source of truth per entity.
- We settle the data model before writing code, which prevents most integration failures.
- Every integration is monitored and documented so it can be maintained without us.
- The warehouse is designed with the AI and analytics work that will follow in mind.
- A retailer whose ERP, commerce platform and warehouse system each hold a different stock figure.
- A bank or insurer with a core platform that every new product must be wired to by hand.
- A group standardising reporting across subsidiaries that run different systems.
- Staff re-key data between systems and reconciliation is a monthly project.
- Point-to-point links have multiplied until nobody dares change one.
- A failed sync is discovered by a customer rather than by a monitor.
- AI and analytics plans are blocked because the data is not in one place.
What is included.
- 01
Integration map
Every system, data flow, owner and failure mode drawn and agreed.
- 02
Data model
Canonical definitions for customer, product, order and the other entities that matter.
- 03
API and middleware build
Integrations built with an iPaaS, custom services or event streams as appropriate.
- 04
Data pipelines and warehouse
ELT pipelines into a warehouse such as BigQuery, Snowflake or Postgres with modelling in dbt.
- 05
Monitoring and error handling
Retries, dead-letter queues and alerts so failures are seen and fixed.
Four steps, no surprises.
- 01
Map
Workshops and system review to produce the integration map and data model.
- 02
Design
Choose patterns and tooling per flow, with security and volume in mind.
- 03
Build
Integrations and pipelines built in sprints with automated tests.
- 04
Run
Monitoring, documentation and hand-over or ongoing support.
From first meeting to steady state.
- 01Weeks 1 to 2
Discovery and map
Workshops and system review produce the integration map and canonical data model.
- 02Weeks 3 to 4
Design
Patterns and tooling chosen per flow with security, volume and ownership agreed.
- 03Weeks 5 to 14
Build
Integrations, pipelines and warehouse built in sprints with automated tests.
- 04Week 15 onwards
Run
Monitoring, documentation and either hand-over or ongoing support.
- Manual re-keying hours removed from the processes in scope.
- Integration failure rate and time to detect.
- Reconciliation differences between systems for the entities in scope.
- Time to add a new system or flow to the integration layer.
- Integration architect
- Data engineer
- Backend developers
- QA engineer
- Delivery lead
- Integration map and data model.
- Built and tested integrations.
- Data warehouse with modelled tables.
- Monitoring and alerting.
- Technical documentation and runbooks.
Integration projects are fixed scope after a short discovery of one to two weeks. Build phases usually run six to sixteen weeks depending on the number of systems. Ongoing maintenance and new flows are handled on a retainer.
Data Engineering & MLOps
Pipelines, feature stores, model deployment and monitoring so AI keeps working after launch.
Web, Webshop & App DevelopmentWebshop & E-commerce Development
Shopify Plus, WooCommerce or headless commerce with the integrations a real store needs.
MarketingAnalytics & Reporting
GA4, server-side tracking and attribution that put every channel on one revenue dashboard.
Systems Integration & Data Infrastructure, in plain terms.
Make, n8n, Workato and Azure Integration Services for standard flows, and custom services where volume, logic or security demand it.
Usually, through its API, database or file exports. The discovery phase confirms what is possible and how reliable it will be.
If more than two systems need to be reported on together, or if you plan to use AI on your data, yes. It is also less work than most people expect.
Data flows are documented for your record of processing, minimised where possible and encrypted in transit and at rest.